Recent interest based relevance scoring

ABSTRACT

A computer-implemented method for processing query information includes receiving prior queries followed by a current query, the prior and current queries being received within an activity period an originating with a search requester. The method also includes receiving a plurality of search results based on the current query. Each search result identifying a search result document, each respective search result document being associated with a query specific score indicating a relevance of the document to the current query. The method also includes determining a first category based, at least in part, on the prior queries. The method also includes identifying a plurality of prior activity periods of other search requesters, each prior activity period containing a prior activity query where the prior activity query matches the current query, and where the prior activity period indicates the same first category. The method also includes obtaining category based selection statistics of the other requesters search results based on the last queries of the other activity periods. The method also includes obtaining general selection statistics of a more general population of requesters based on the current query. The method also includes generating adjusted scores for the search result documents by adjusting the respective scores based on the query specific score and the category specific score. The method also includes ranking the search result documents according to the respective adjusted scores.

BACKGROUND

The present disclosure relates to using queries provided by one ormultiple users to identify the intent of the user or users.

Internet search engines aim to identify documents or other items thatare relevant to a user's queries and to present the documents or itemsin a manner that is most useful to the user. Such activity ofteninvolves a fair amount of inferring from various clues of what the userwants. Certain clues may be user specific. For example, knowledge that auser is making a request from a mobile device, and knowledge of thelocation of the device, can result in much better search results forsuch a user.

Clues about a user's needs may also be more general. For example, searchresults can have an elevated importance, or inferred relevance, if anumber of other search results link to them. If the linking results arethemselves highly relevant, then the linked-to results may have aparticularly high relevance. Such an approach to determining relevancemay be premised on the assumption that, if authors of web pages feltthat another web site was relevant enough to be linked to, then websearchers would also find the site to be particularly relevant. Inshort, the web authors “vote up” the relevance of the sites.

Other various inputs may be used instead of, or in addition to, suchtechniques for determining and ranking search results. For example, userreactions to particular search results or search result lists may begauged, so that results on which users often click will receive a higherranking. The general assumption under such an approach is that searchingusers are often the best judges of relevance, so that if they select aparticular search result, it is likely to be relevant, or at least morerelevant than the presented alternatives.

SUMMARY

This specification describes technologies relating to ranking ofresources for presentation in search results. In general, one or moreaspects of the subject matter described in this specification can beembodied in one or more methods for processing query information. Themethods include receiving prior queries followed by a current, the priorand current queries being received within an activity period andoriginating with a search requester. The methods include receiving aplurality of search results based on the current query, each searchresult identifying a search result document, each respective searchresult document being associated with a query specific score indicatinga relevance of the document to the current query. The methods includedetermining a first category based, at least in part, on the priorqueries. The methods include identifying a plurality of prior activityperiods of other search requesters. Each prior activity period containsa prior activity query where the prior activity query matches thecurrent query and where the prior activity period indicates the samefirst category. The methods include obtaining category based selectionstatistics of the other requesters search results based on the prioractivity queries of the other activity periods. The methods includeobtaining general selection statistics of a more general population ofrequesters based on the current query. The methods include generatingadjusted scores for the search result documents by adjusting therespective scores based on the query specific score and the categoryspecific score. The methods include ranking the search result documentsaccording to the respective adjusted scores.

These and other embodiments can optionally include one or more of thefollowing features:

Determining that the prior queries indicate the first category may alsoinclude analyzing time spent between selecting a first result and asecond result. Generating may include determining a category specificselection frequency measuring the frequency with which the respectivesearch result document is selected in the other activity periods,determining a general selection frequency measuring the frequency withwhich the respective search result document is selected by the moregeneral population; and comparing the category specific selectionfrequency and the general selection frequency. Comparing may alsoinclude determining the difference between the category specificselection frequency and the general selection frequency. Comparing mayalso include determining that the difference exceeds a given threshold.Generating may also include comparing a selection value for the searchresult document and a total selection value aggregated across searchresult documents in the first category of search requester interest. Theselection count for the search result may be weighted based on timebetween selecting the search result and at least one of a selection ofsuccessive search results and other user activity marking the end of theuser engagement with the search result. Adjusting the respective scoresbased on the category specific score may include adjusting therespective score by multiplying and summing the category specific scoreand the multiplied value to the respective score. Determining a firstcategory may include determining a second category. Determining thefirst category of interest may be based on search requester specificinformation. The search requester specific information may includedemographic information and/or location information. Generating thecategory specific score for the search result documents may includedetermining an absence of the category specific score for statisticallyinsignificant categories.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, objects, and advantages of thesubject matter will be apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows an example information retrieval system.

FIG. 2 shows example components of an information retrieval system.

FIG. 3 shows another example information retrieval system.

FIG. 4 illustrates an information retrieval search session.

FIG. 5 shows a flowchart of operations of an interest identifier.

FIG. 6 shows a flowchart of operations of a search system.

FIG. 7 shows a schematic diagram of an example computer system.

Like reference symbols and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 shows an example system 1000 for improving the relevance ofresults obtained from submitting search queries as can be implemented inan internet, intranet, or other client/server environment. The system1000 is an example of an information retrieval system in which thesystems, components and techniques described below can be implemented.Although several components are illustrated, there may be fewer or morecomponents in the system 1000. Moreover, the components can bedistributed on one or more computing devices connected by one or morenetworks or other suitable communication mediums.

A search requester 1002 (1002 a, 1002 b, 1002 c) can interact with thesystem 1000 through a client device 1004 (1004 a, 1004 b, 1004 c) orother device. For example, the client device 1004 can be a computerterminal within a local area network (LAN) or wide area network (WAN).The client device 1004 can include a random access memory (RAM) 1006 (orother memory and/or a storage device) and a processor 1008. Theprocessor 1008 is structured to process instructions within the system1000. In some arrangements, the processor 1008 is a single-threadedprocessor. In other arrangements, the processor 1008 is a multi-threadedprocessor. The processor 1008 can include multiple processing cores andis structured to process instructions stored in the RAM 1006 (or othermemory and/or a storage device included with the client device 1004) todisplay graphical information for a search requester interface.

A search requester 1002 a can connect to a search engine 1030 within aserver system 1014 to submit a query 1015. When the search requester1002 a submits the query 1015 through an input device attached to aclient device 1004 a, a client-side query signal 1010 a is sent into anetwork 1012 and is forwarded to the server system 1014 as a server-sidequery signal 1010 b. Server system 1014 can be one or more serverdevices in one or more locations. The server system 1014 includes amemory device 1016, which can include the search engine 1030 loadedtherein. A processor 1018 is structured to process instructions withinthe system 1014. These instructions can implement one or more componentsof the search engine 1030. The processor 1018 can be a single-threadedprocessor or a multi-threaded processor, and can include multipleprocessing cores. The processor 1018 can process instructions stored inthe memory 1016 related to the search engine 1030 and can sendinformation to the client device 1004, through the network 1012, tocreate a graphical presentation in a search requester interface of theclient device 1004 (e.g., a search results web page displayed in a webbrowser).

The server-side query signal 1010 b is received by the search engine1030. The search engine 1030 uses the information within the searchrequester query 1015 (e.g. query terms) to find relevant documents. Thesearch engine 1030 can include an indexing engine 1020 that activelysearches a corpus (e.g., web pages on the Internet) to index thedocuments found in that corpus, and the index information for thedocuments in the corpus can be stored in an index database 1022. Thisindex database 1022 can be accessed to identify documents related to thesearch requester query 1015. Note that, an electronic document (whichfor brevity will simply be referred to as a document) does notnecessarily correspond to a file. A document can be stored in a portionof a file that holds other documents, in a single file dedicated to thedocument in question, or in multiple coordinated files.

The search engine 1030 can include a ranking engine 1052 to rank thedocuments related to the search requester query 1015. The ranking of thedocuments can be performed using traditional techniques for determiningan information retrieval (IR) score for indexed documents in view of agiven query. The relevance of a particular document with respect to aparticular search term or to other provided information may bedetermined by any appropriate technique. For example, the general levelof back-links to a document that contains matches for a search term maybe used to infer a document's relevance. In particular, if a document islinked to (e.g., is the target of a hyperlink) by many other relevantdocuments (e.g., documents that also contain matches for the searchterms) then it can be inferred that the target document is particularlyrelevant. This inference can be made because the authors of the pointingdocuments presumably point, for the most part, to other documents thatare relevant to their audience.

If the pointing documents are in turn the targets of links from otherrelevant documents, they can be considered more relevant, and the firstdocument can be considered particularly relevant because it is thetarget of relevant (or even highly relevant) documents. Such a techniquemay be the determinant of a document's relevance or one of multipledeterminants. The technique is exemplified in some systems that treat alink from one web page to another as an indication of quality for thelatter page, so that the page with the most such quality indicators israted higher than others. Appropriate techniques can also be used toidentify and eliminate attempts to cast false votes so as toartificially drive up the relevance of a page.

Various types of information may be provided to the rank modifier engine1056 for improving the ranking of documents. For example, searchrequesters are often interested in one or more particular subjects orcategories for a short time period, e.g., the last 30 minutes or twohours of a search session. The short-term interests of a searchrequestor may be identified and used to adjust ranking of searchresults. To determine such underlying interests of a search requester,the search engine 1030 can include an interest identifier 1058 that mayimplement one or more interest identification techniques. For example,the categories of interest may be gleaned from adjustments provided bythe search requester. A search requester's interested in a particularcategory may issue queries related to the category and select searchresults referring to resources related to the category. A searchrequester may have a browsing history that indicates a particular areaof interest. Generally, the search requester's recent activity canindicate the search requester's present needs and interests. In somearrangements, after an initial search request is executed andcorresponding results provided, the search may be refined by the searchrequester to steer the subsequent search towards desired results. Forexample, adding or removing particular search terms and phrases during asearch session may provide clues to the interests of the searchrequester 1002 a (regarding the search). Similarly, the manner in whichan individual interacts with search results (e.g., search selections,time spent interacting with search selections, advertisement selections,etc.) may enable the interest identifier 1058 to identify one or morecategories of search requester interest. In some arrangements, aninterest identifier may look at information over a recent time period.(for example, the last 30 minutes, one hour, two hours, or twenty-fourhours) to determine a short-term category of user interest. In somearrangements, the search system infers that a search requester is notinterested in a particular category based on the search requester'srecent activity, e.g., the search requester ignores a search resultreferring to a resource related to the particular category, or thesearch requester views the resource for a very short period of timeafter selection of the search result.

Additional information may also be used in conjunction with therefinement information for identifying search requester interest. Forexample, the language of the search requester (e.g., English, Spanish,etc.), the location of the search requester (e.g., country, region,state, city, etc.) and similar information may be utilized. Onceidentified, data representing the identified interest may be catalogedin a database (e.g., the index db 1022). Further, the identified searchrequester interest may be used for various applications such asproviding assistance during future search sessions performed by thesearch requester 1002 a or other search requesters. Search resultscoring and ranking (e.g., as performed by the ranking engine 1020 orthe rank modifier engine 1056) can be adjusted to account for interestof the search requester 1002 a or similar search requesters.

In some arrangements, the ranking engine 1052 receives one or moresignals (i.e., data indicative of relevance) from a rank modifier engine1056 to assist in determining an appropriate ranking for the resources.The rank modifier engine 1056 can provide one or more measures ofcategory relevance for the resources, which can be used by the rankingengine 1052 to improve the ranking of resources referred to by searchresults 1028 provided to the search requester 1002. In somearrangements, the measure of category relevance represents a probabilityof search result selection given the values of one or more features, asdescribed further below. The rank modifier engine 1056 can perform oneor more of the operations described below to generate the one or moremeasures of category relevance.

In some arrangements, the search engine 1030 includes a scoring enginethat generates scores for resources based on many different features,including content based features that indicate the relevance of resourcecontent to the query and query independent features that generallyindicate the quality of resources. The ranking engine 1052 can produce aranking of resources based on scores received from the scoring engineand one or more measures of category relevance from the rank modifierengine 1056.

The search engine 1030 can forward the final, ranked result list withina server-side search results signal 1028 a through the network 1012.Exiting the network 1012, a client-side search results signal 1028 b canbe received by the client device 1004 a where the results can be storedwithin the RAM 1006 and/or used by the processor 1008 to display theresults on an output device for the search requester 1002 a.

FIG. 2 shows example components of an information retrieval system.These components can include an indexing engine 2010, a scoring engine2020, a ranking engine 2030, a rank modifier engine 2070, and aninterest identifier 2080. The indexing engine 2010 can function asdescribed above for the indexing engine 1020. In addition, the scoringengine 2020 can generate scores of document results based on manydifferent features, including content-based features that link a queryto document results, and query-independent features that generallyindicate the quality of document results. The content-based features caninclude aspects of document format, such as query matches to title oranchor text in an HTML (Hyper Text Markup Language) page. Thequery-independent features can include aspects of documentcross-referencing, such as a rank of the document or the domain.Moreover, the particular functions used by the scoring engine 2020 canbe tuned to adjust the various feature contributions to the final IRscore using automatic or semi-automatic processes.

The ranking engine 2030 can produce a ranking of document results 2040for display to a search requester based on IR scores received from thescoring engine 2020 and one or more signals from the rank modifierengine 2070. The rank modifier engine 2070 can adjust rankings, at leastin part, based on data received from the interest identifier 2080. Alongwith being provided data from the result selection logs 2060, othersources may provide information to the interest identifier 2080. Forexample, queries entered into a search requester interface may beprovided to the interest identifier 2080. The interest identifier 2080may provide information about the relevance of particular search resultsfor a category of interest and a query to the rank modifier engine 2070.In this particular example, the interest identifier 2080 providesinformation to the rank modifier engine 2070, however otherarchitectures may be implemented. For example, a category of interestinformation may be provided by the interest identifier 2080 to theindexing engine 2010 or one or more other components of the informationretrieval system. A tracking component 2050 can be used to recordinformation regarding individual search requester selections of theresults presented in the ranking 2040. For example, the trackingcomponent 2050 can be embedded JavaScript code included in a web pageranking 2040 that identifies search requester selections (clicks) ofindividual document results and also identifies when the searchrequester returns to the results page. Therefore the component mayindicate the amount of time the search requester spent viewing theselected document result. In other arrangements, the tracking component2050 can be a proxy system through which search requester selections ofthe document results are routed, or the tracking component can includepre-installed software at the client (e.g., a toolbar plug-in to theclient's operating system). Other arrangements are also possible, suchas using a feature of a web browser that allows a tag/directive to beincluded in a page, which requests the browser to connect back to theserver with message(s) regarding link(s) clicked by the searchrequester.

The recorded information can be stored in result selection log(s) 2060.The recorded information can include log entries that indicate, for eachsearch requester selection, the query, the resource, the time on theresource, the language employed by the search requester, and the countryor region where the search requester is likely located (e.g., based onthe server used to access the IR system). Other information can also berecorded regarding search requester interactions with a presentedranking, including negative information, such as the fact that adocument result was presented to a search requester but was not clicked,position(s) of click(s) in the search requester interface, IR scores ofclicked results, IR scores of all results shown before the clickedresult, the titles and snippets shown to the search requester before theclicked result, the search requester's cookie, cookie age, IP (InternetProtocol) address, search requester agent of the browser, etc. Still,further information can be recorded such as described below duringdiscussion of the various features that can be used to build a priormodel. Moreover, similar information (e.g., IR scores, position, etc.)can be recorded for an entire session, or multiple sessions of a searchrequester, including potentially recording such information for everyclick that occurs both before and after a current click.

The information stored in the result selection log(s) 2060 can be usedby one or more components of the information retrieval system. Forexample, information could be provided to interest identifier 2080 andthe rank modifier engine 2070 in generating one or more signals to theranking engine 2030. In general, a wide range of information can becollected and used to modify or tune the click signal from the searchrequester to make the signal, and the future search results provided, abetter fit for the search requester's needs. Thus, search requesterinteractions with the rankings presented to the search requesters of theinformation retrieval system can be used to improve future rankingsAdditionally, query adjustments indicative of refining a search can beused to modify rankings. In some arrangements, the search requesterinteraction and search requester interest data may be provided to one ormore server systems (e.g., server system 1014) for use and storage(e.g., database 1022) for later retrieval. For example, interestidentifier 2080 may compare current search results with previous searchactivity to provide ranking adjustments to the rank modifier engine2070.

The components shown in FIG. 2 can be combined in various manners andimplemented in various system configurations. For example, the scoringengine 2020 and the ranking engine 2030 can be merged into a singleranking engine, such as the ranking engine 1052 of FIG. 1. The interestidentifier 2080, the rank modifier engine 2070, and the ranking engine2030 can also be merged and, in general, a ranking engine includes anysoftware component that generates a ranking of document results after aquery. Moreover, a ranking engine can be included in a client system inaddition to (or rather than) in a server system.

FIG. 3 shows another example of an information retrieval system. In thissystem, a server system 3050 includes an indexing engine 3060 and ascoring/ranking engine 3070. A client system 3000 includes a searchrequester interface for presenting a ranking 3010, a tracking component3020, result selection log(s) 3030 and a ranking/rank modifierengine/interest identifier 3040. For example, the client system 3000 caninclude a company's enterprise network and personal computers, in whicha browser plug-in incorporates the ranking/rank modifier engine/intentidentifier 3040. When an employee in the company initiates a search onthe server system 3050, the scoring/ranking engine 3070 can return thesearch results along with either an initial ranking or the actual IRscores for the results. The browser plug-in can then re-rank the resultslocally based on tracked page selections for the company-specific searchrequester base. While FIGS. 2 and 3 provide two exemplary informationretrieval systems, other architectures may be implemented. For example,an indent identifier may be positioned in other locations of aninformation retrieval system or distributed across multiple locations.

Referring to FIG. 1, the search system 1014 can modify the IR score of aresource for a particular searcher based on the searcher's inferredshort-term categorical interests. In particular, for a given queryissued by a particular searcher with inferred categorical interests, thesearch system 1014 can refer to the search result selections of othersearchers who issued the same query or similar queries and wereinterested in the same categories. The search system 1014 can increasethe IR scores of the resources associated with the search results thatwere preferred by these similarly interested search requesters.Likewise, the search system 1014 can decrease the IR scores of theresources associated with the search results that were ignored or notpreferred by these similarly interested search requesters. Preferencefor a search result can be inferred in a number of ways, e.g., from ahigh rate of search requester selection of the search result or longviews of the associated resource by search requesters selecting thesearch result.

When the score of a resource increases or decreases, the rank of theresource relative to other responsive resources may be increased ordecreased. Whether the rank of a particular resource changes depends onthe rank of the resource before modifying the score, how much the scoreis modified, and how much the score for neighboring resources aremodified. For example, if the ranking engine 1052 has already ranked aparticular resource as the top resource for a given query, increasingthe score for that particular resource will not change the resource'srank. However, if the search system increases the score for thesecond-ranked resource for the given query, the amount increased mightbe large enough to boost the second-ranked resource to the top spot.Thus, the search system can promote or demote resources in a searchresults list for a particular search requester based on the searchrequester's inferred short-term categorical interests.

In some arrangements, the search system 1014 only modifies the score ofa resource for a particular search requester issuing a given query ifsimilarly interested search requesters selected the search resultreferring to the resource at a rate that differs (e.g., by astatistically significant amount) from the rate at which the generalpopulation selected the same search result returned for the same query.The required threshold to determine that the result is selected moreoften for search requesters with a specific interest than by searchrequesters with a more general interested may be a fixed threshold (forexample a difference of at least 0.01 in absolute click through rate).The required threshold may be relative, for example, the click throughrate may need to be at least 10% greater. For example, if a resultreceives a click through rate of 10% among the general population, and aclick through rate of 12% among search requesters having a particularinterest, the relative difference is 20%. The threshold may also bestatistical. For example, if the click through rate based on a sampleobservation period for a given result is more than a standard deviationgreater for users having a particular interest than for the generalpopulation. In some arrangements the threshold may be a combination ofabsolute, relative, and/or statistical thresholds.

The search system 1014 can use the rate at which search requestersselect a particular search result for a given query as a measure of therelevance of the associated resource to the given query. For example, ifsearch requesters with an identified interest select a search resultmore frequently then the general population, the search system can inferthat other similarly interested search requesters will find the searchresult to be more relevant. Likewise, if search requesters with anidentified interest select a search result less frequently than thegeneral population, the search system can infer that other similarlyinterested search requesters will find the associated resource to beless relevant.

In some arrangements, the difference between the two rates must begreater than a predefined threshold for the search system to modify theresource's score. If the difference is greater than the predefinedthreshold, the search system modifies the score of the resource toreflect the measure of category relevance (e.g., the expected selectionrate) of the resource to the given query for the similarly interestedsearch requesters.

For example, referring to FIG. 4A, a search requester may be researchingbusiness schools during a particular period. During this period, thesearch requester issues the query “harvard business” 420 a in the queryinterface 404. As shown by process arrows 422 a and 424 a, the searchquery 420 a and characteristics associated with the user (e.g. language,location, etc.) may be sent to the server side system 402 via thenetwork 412. The server side system 402 can process the search query 420a and related information, for example by using one or more softwaremodules (e.g., a search engine) executed by the server 408. As shown byprocess arrow 426 a, the “harvard business” query 420 a may be stored inthe data store 410. Other information (e.g., index information, usercharacteristics, search session statistics, etc.) may also be retrievedfrom or stored in the data store 410 and can be used by the server 408for providing a set of query results. The data store 410 isrepresentative of various types of information sources that may be incommunication with the server 408. For example, one or more storagedevices (e.g., hard drives, etc.), servers, and computing deviceequipment may be directly or indirectly (e.g., via one or more networks)in communication with the server 408. As shown by process arrows 428 aand 430 a, search results may be sent to the client side system 400 viathe network 412. Upon receipt, a set of search results 440 a may bepresented to a user via the results interface 406. In this example, thesearch requester selects the “Harvard Business School” search result432, referring to the Harvard Business School web site. As shown byprocess arrows 434 a and 436 a, information about the selection is sentto the server side system 402 and stored in the database 410.

Referring to FIG. 4B, during the same period, the same search requesterissues a query “kellogg” 420 b in the query interface 404. As shown byprocess arrows 424 b and 424 b, the search query 420 b andcharacteristics associated with the user may be sent to the server sidesystem 402 via the network 412. The server side system 402 may processthe search query 420 a and related information. As shown by processarrow 426 a, the query “kellogg” 430 b may be stored in the data store410. Other data may be retrieved from or stored in the data store 410,including information about the previously submitted query 420 a and thepreviously selected result 432 a. In this example, the server 408determines that there is insufficient data to support an inference of acategory of interest. In other examples, the previously submitted query420 a, the search results returned from executing the query, and/or thepreviously selected result 432 a alone, or coupled with the userinformation, may be sufficient for the server 408 to infer a category ofuser interest. As shown by process arrows 428 b and 430 b, searchresults may be sent to the client side system 400 via the network 412.Upon receipt, a set of search results 440 b may be presented to a uservia the results interface 406. In this example, the search requesterselects the “Kellogg School of Management” search result 432 b referringto the website of the Kellogg School of Management at Northwestern. Asshown by process arrows 434 b and 436 b, information about the selectionis sent to the server side system 402 and stored in the database 410.

Referring to FIG. 4C, also during the same period, the same searchrequester then issues a query “anderson” 420 c in the query interface404. As shown by process arrows 424 c and 424 c, the search query 420 cand characteristics associated with the user may be sent to the serverside system 402 via the network 412. The server side system 402 canprocess the search query 420 c and related information. As shown byprocess arrow 426 c, data may be retrieved from or stored in the datastore 410, including information about the previously submitted queries420 a, 420 b and the previously selected results 432 a, 432 b. Theserver 408, infers from the available data that the search requester hasan interest in “business schools.” The server 408 computes or retrievespreviously computed selection statistics for the search results returnedfor the query “anderson” 420 c for both search requesters of the generalpopulation and search requesters with an inferred interest in “businessschools.” The selection statistics might indicate that search requestersinterested in “business schools” selected the UCLA Anderson School ofManagement search result 432 c at a higher rate (e.g., 87%) than therate (e.g., 2.7%) at which search requesters of the general populationselected this search result returned for the query “anderson.” If thedifference in the two rates exceeds a predefined threshold, the server408 may increase the score for the UCLA Anderson School of Managementweb site for this search requester, where the increase reflects thehigher measure of category relevance of this resource for similarlyinterested search requesters. As shown by process arrows 428 c and 430c, search results may be sent to the client side system 400 via thenetwork 412. Upon receipt, a set of search results 440 c may bepresented to a user via the results interface 406. As a result of theincrease, the UCLA Anderson School of Management search result 432 c mayappear higher on the list of search results returned for the query“anderson”420 c for this particular search requester relative to thesearch result's position on the list of search results returned for thequery for search requesters of the general population.

Referring to FIG. 5, a flow chart 5000 represents a particulararrangement of operations of the interest identifier (FIG. 1, 1058).Typically the operations are executed by one or more processors of acomputer system upon which the interest identifier is resident. Whiletypically executed by a single electronic device, in some arrangementsoperation execution may be distributed among two or more electronicdevices (for example, computer systems).

Operations include storing recent activity 5010. In some arrangementsrecent activity may be stored by the interest identifier 1058 directly.In other arrangements, another component of the server system 1014 maystore the recent activity for later use by the interest identifier 1058.In one example, a search requester's browing history may be stored. Inanother example, a search requester's interactions with the searchsystem are stored. Their interaction may include queries submitted by asearch requester and information descriptive of a search requester'sinteractions with the provided search results. For example, a searchrequester may select a particular resource and perform some action whichindicates the search requester in finished with the resource, such asselecting another result, entering a new query, or leaving the searchpage or selecting another result. The selected results may be storedalong with a measure of the passage of time between the selection andthe subsequent action. As another example, a search requester may submita query and subsequently submit a second query which further refines thequery. In another arrangement, information about the particular searchrequester may also be stored. For example, the system may storelocation, demographic, and linguistic information.

Operations also include identifying categories of interest 5020. In oneexample, the system may use the stored activity to identify categoriesof interest. In some arrangements the system may examine a searchrequester's interactions with the system to determine a category ofinterest. This may include analyzing patterns of query submission andresult selection to infer a category. In some arrangements, the shortterm categorical interests are identified using a technique forclassifying text (e.g., query terms or text in resources) into finegrained hierarchical categories. A hierarchical category may consist ofa series of progressively narrower categories in a parent/childrelationship, for example, a “sports” category may have a “football”child category, which in turn may have “high school football” and“professional football” child categories. Generally, the depth of thehierarchy defines the level of refinement. In some arrangements a searchrequester's category of interest may be defined at any level of thehierarchy. For example, a search requester may be determined to beinterested in the “high school football” or a search requester may bedetermined to be interested in “sports.” In other arrangements, acategory of interest may only be defined at the narrowest levelsubcategory, for example, “high school football”. In some arrangements,a determined interest in a parent category (for example, “football”)indicates interest in all child categories for that parent (for example,“professional football” and “high school football”). In otherarrangements, interest in a parent category indicates only a generalinterest and does not indicate interest in the child categories, forexample, an interest in “sports” may not indicate an interest in“football” or “high school football.”

In some arrangements, categories are formed by comparing a searchrequesters queries with the search results the search requester selects.For example, for the search requester submissions of query “professionalfootball” a search engine may present a URL pointing to document to thesearch requester a number of times. If a search requester clicks on theURL, the click can be considered a confirmation that document is a validsearch result for query. In this manner, categories of interest may beformed based on the queries and the search requester's selections. Insome arrangements, queries which produce similar results or similarselected results may be determined to refer to the same category ofinterest.

Operations also include determining selection counts 5030. In oneexample, determining selection counts may include calculating the numberof times a resource is selected by a search requester executing aparticular query and having a particular category of interest. Theresults of this process may be stored for later access. In somearrangements, selection counts are further refined based on thelocation, demographic or linguistic information.

In some arrangements, the selection count may be weighted based on thetime between a search requester selects the respective resource and whenthe search requester indicates that the requester is finished with therespective source. In some arrangements, the selection of a subsequentresource by a search requester indicates that the requester is finishedwith the first resource. For example, if a search requester selects theresource referring to a particular resource and views that resource fora long period of time before selecting a different resource, theselection count may be increased by an amount proportional to how longthe requester waited before selecting the different resource. In somearrangements submitting a new query may indicate that a search requesteris finished with the first resource. In still other arrangements, asearch requester may indicate that the search requester is finished witha particular resource by not returning to the search results.

Operations also include calculating a category click-fraction 5040. Inone example, the category click-fraction may be calculated for eachresource selected for a particular query and category of interest. Thecategory click-fraction may be a measure of the selection count for aparticular resource as a percentage of the combined selection count forall resources for the query and category of interest.

Operations also include calculating a general click fraction 5050. Inone example, the general click fraction may be a measure of theselection count for a particular resource as a percentage of thecombined selection count for all resources for the query independent ofany category of interest.

Operations also include calculating category relevance 5060. In oneexample, the category relevance may be a measure of the differencebetween category click fraction and the general click fraction. Thedifference may be determined in either absolute or relative terms.

Operations also include determining if the category is significant 5070.In one example, the significance of the category may be determined basedon the category relevance, the category click fraction, and the generalclick fraction. In one arrangement, the category may be significant ifthe category relevance exceeds a certain threshold. In another example,the category may be significant if the difference between the categoryclick fraction and the general click fraction is statisticallysignificant. In other arrangements, a category may be insignificant,regardless of any click fraction, if an aggregated selection count forall queries and resources is less than a predefined threshold.

Operations also include providing a category relevance score 5080. Inone example, the category relevance score may be determined based on thecategory relevance. In one arrangement, the category relevance score maybe determined by multiplying the category relevance by a constant value.The category relevance score may be provided to a ranking engine andstored for later use.

In some arrangements, the interest identifier 1058 may execute theprocess 5000 in batch. The interest identifier 1058 may cache themeasures of category relevance for quick retrieval. The interestidentifier 1058 may modify the score of resources matching a given queryfor a particular search requester as soon as the system infers one ormore categories of interests for the search requester. The interestidentifier 1058 may also cache other scoring information, for example,measures of category relevance, general click fractions or categoryclick fractions.

Referring to FIG. 6, a flowchart 6000 represents a particulararrangement of operations of the interest identifier (FIG. 1, 1058).Typically the operations are executed by one or more processors of acomputer system upon which the search system is resident. Whiletypically executed by a single electronic device, in some arrangements,operation execution may be distributed among two or more electronicdevices (for example, computer systems).

Operations include reviewing recent activity 6010. In one example, theinterest identifier 1058 reviews the recent activity of a searchrequester. The recent activity can be queries issued and the resourcesselected within a short time period, e.g., during the last 30 minutes,two hours, or 24 hours of a search session. Recent activity can also oralternatively be defined as a sequence of queries and interactions withresources with no interruption of more than a predetermined amount oftime. Recent activity can also be determined in terms of the currenttask. In some arrangements, the end of the task may be identified whenthe current queries are no longer consistent with the previouslyidentified category of interest.

Operations also include identifying categories of interest 6020. Theinterest identifier 1058 may identify at least one category of interestfor the search requester based on the search requester's recentactivity. The system can identify the categories using the techniqueused in process 5000 described above. In some arrangements, the categoryof interest is further identified using location, demographic, andlinguistic information of the search requester.

Operations also include receiving a current query 6030. In one example,the interest identifier 1058 receives a new query issued by the searchrequester.

Operations also include identifying resources 6040. In one example, theinterest identifier 1058 identifies multiple resources matching thesearch requester's current query. Each resource is provided with a IRscore which measures the general relevance of the resource to thecurrent query. In some arrangements, resources matching particularpopular queries are predetermined and cached. If available the systemretrieves a list of the cached resources and their scores.

Operations also include collecting category relevance scores 6050. Inone example, the interest identifier 1058 retrieves category relevancescores for each resource matching the search requester's current querybased on the category of interest.

Operations also include determining adjusted relevance scores 6060.While this operation may be performed by the interest identifier 1058,it is typically performed by the ranking engine 1052. In one example,the ranking engine 1052 adjusts IR scores based on the categoryrelevance score provided by the interest identifier 1058. In onearrangement, the adjusted relevance score is determined by summing theIR score and the category relevance score. In other arrangements, theadjusted score is determined by multiplying the IR score and thecategory relevance score.

In some arrangements, the relevance score of a resource may have beenpreviously adjusted based on other characteristics of the searchrequester independent of any category of interest. For example, arelevance score may be adjusted based on the search requester'slocation, linguistic, and demographic information. The interestidentifier 1058 may further adjust the relevance score of the resourceby applying an adjustment that replaces the previous adjustments andaccounts for both the characteristics of the search requester andcategories of interest.

In some arrangements, the interest identifier 1058 may identify multiplecategories of interest for the search requester. The interest identifier1058 then determines a category relevance score for each category ofinterest. The final category relevance score reflects a combination ofthe relevance of the multiple categories. In some arrangements, thecategory relevance score for multiple categories may be based on summingthe click fractions for each category. In other arrangements, thecategory relevance score may be based on the sum of all the categoryrelevance scores. In other arrangements, the category relevance formultiple categories may be based on the multiplication of each categoryclick fraction.

FIG. 7 is a schematic diagram of an example computer system 7000. Thesystem 7000 can be used for practicing operations described above. Thesystem 7000 can include a processor 7010, a memory 7020, a storagedevice 7030, and input/output devices 7040. Each of the components 7010,7020, 7030, and 7040 are interconnected using a system bus 7050. Theprocessor 7010 is capable of processing instructions within the system7000. These instructions can implement one or more aspects of thesystems, components and techniques described above. In someimplementations, the processor 7010 is a single-threaded processor. Inother implementations, the processor 7010 is a multi-threaded processor.The processor 7010 can include multiple processing cores and is capableof processing instructions stored in the memory 7020 or on the storagedevice 7030 to display graphical information for a user interface on theinput/output device 7040.

The memory 7020 is a computer readable medium such as volatile or nonvolatile that stores information within the system 7000. The memory 7020can store processes related to the functionality of the search engine1030 (shown in FIG. 1). The storage device 7030 is capable of providingpersistent storage for the system 7000. The storage device 7030 caninclude a floppy disk device, a hard disk device, an optical diskdevice, a tape device, or other suitable persistent storage mediums. Thestorage device 7030 can store the various databases described above. Theinput/output device 7040 provides input/output operations for the system7000. The input/output device 7040 can include a keyboard, a pointingdevice, and a display unit for displaying graphical user interfaces.

The computer system shown in FIG. 7 is but one example. In general,embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing apparatus” encompassesall apparatus, devices, and machines for processing data, including byway of example a programmable processor, a computer, or multipleprocessors or computers. The apparatus can include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them. A propagated signal is anartificially generated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well. For example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback, and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some cases,be excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter described in thisspecification have been described. Other embodiments are within thescope of the following claims. For example, the actions recited in theclaims can be performed in a different order and still achieve desirableresults. As one example, the processes depicted in the accompanyingfigures do not necessarily require the particular order shown, orsequential order, to achieve desirable results. In certain arrangements,multitasking and parallel processing may be advantageous. In somearrangements, the search system uses a search requester's inferredshort-term categorical interests to identify resources that match thesearch requester's present query.

What is claimed is:
 1. A non-transitory computer readable storage mediumhaving a computer program stored thereon, the program comprisinginstructions that, when executed by data processing apparatus, cause thedata processing apparatus to perform operations comprising: analyzing arecent search activity period of a user to determine a short-termcategory of interest of the user, wherein the analyzing comprisescomparing queries submitted by the user during the recent searchactivity period with categories of selected search results that wereselected by the user during the recent search activity period; obtaininga plurality of search results responsive to a query, each of theplurality of search results having a respective score; for eachparticular search result of a first plurality of the search results:calculating a category selection value for the particular search result,wherein the category selection value for the particular search result isbased on a measure of a count of selections of the particular searchresult as a portion of combined selection counts for search resultsresponsive to the query for a plurality of users for the short-termcategory of interest; calculating a general selection value for theparticular search result, wherein the general selection value for theparticular search result is based on a measure of a count of selectionsof the particular search result as a portion of combined selectioncounts for search results responsive to the query for a plurality ofusers for any category of interest; calculating a category relevance forthe particular search result, wherein the respective category relevancefor the particular search result is based on a difference between therespective category selection value for the search result and therespective general selection value for the particular search result;selecting one or more search results of the plurality of search results,each selected search result being selected based on the selected searchresult having a category relevance that exceeds a threshold; adjustingthe respective score for each of the selected search results based on,at least, the category selection value for each selected search resultand the general selection value for each selected search result; andranking the search results of the plurality of search results accordingto the respective adjusted scores for the selected search results andthe respective scores for search results that were not selected.
 2. Thenon-transitory computer-readable storage medium of claim 1, whereinanalyzing the recent search activity period of the user comprisesdetermining an amount of time elapsed between selection of a firstsearch result in the recent search activity period and selection of asecond search result in the recent search activity period.
 3. Thenon-transitory computer-readable storage medium of claim 1, wherein eachof one or more of the selections of the particular search result isweighted by a respective weight based on an amount of time that a userviewed a document referred to by the particular search result.
 4. Thenon-transitory computer-readable storage medium of claim 1, wherein thecategory relevance for each selected search result is statisticallysignificant.
 5. The non-transitory computer-readable storage medium ofclaim 1, wherein adjusting the respective score for a particularselected search result comprises combining the category relevance forthe particular selected search result with an information retrievalscore of the particular selected search result.
 6. The non-transitorycomputer-readable storage medium of claim 5, wherein combining thecategory relevance for the selected search result with the informationretrieval score of the particular selected search result comprises:multiplying the category relevance for the selected search result by theinformation retrieval score of the particular selected search result, oradding the category relevance for the particular selected search resultby the information retrieval score of the particular selected searchresult.
 7. The non-transitory computer-readable storage medium of claim1, wherein determining the short-term category of interest comprisesdetermining the short-term category of interest based on, at least, userspecific information.
 8. The non-transitory computer-readable storagemedium of claim 7, wherein the user specific information includes atleast one of demographic information of the user and locationinformation of the user.
 9. The non-transitory computer readable storagemedium of claim 1, wherein: the category selection value for theparticular search result is a first click-through rate for theparticular search result when the particular search result is presentedin response to the query and to a first group of users having aninterest that matches the short-term category of interest; the generalselection value for the particular search result is a secondclick-through rare for the particular search result when the particularsearch result is presented in response to the query and to a secondgroup of users that have any category of interest, the second group ofusers having at least one user that is not included in the first groupof users; and the category relevance is based on a difference betweenthe first click-through rate and the second click-through rate.
 10. Acomputer-implemented method comprising: analyzing a recent searchactivity period of a user to determine a short-term category of interestof the user, wherein the analyzing comprises comparing queries submittedby the user during the recent search activity period with categories ofselected search results that were selected by the user during the recentsearch activity period; obtaining a plurality of search resultsresponsive to a query, each of the plurality of search results having arespective score; for each particular search result of a first pluralityof the search results: calculating a category selection value for theparticular search result, wherein the category selection value for theparticular search result is based on a measure of a count of selectionsof the particular search result as a portion of combined selectioncounts for search results responsive to the query for a plurality ofusers for the short-term category of interest; calculating a generalselection value for the particular search result, wherein the generalselection value for the particular search result is based on a measureof a count of selections of the particular search result as a portion ofcombined selection counts for search results responsive to the query fora plurality of users for any category of interest; calculating acategory relevance for the particular search result, wherein therespective category relevance for the particular search result is basedon a difference between the respective category selection value for thesearch result and the respective general selection value for theparticular search result; selecting a one or more search results of thefirst plurality of search results, each selected search result beingselected based on the selected search result having a category relevancethat exceeds a threshold; adjusting the respective score for each of theselected search results based on, at least, the category selection valuefor each selected search result and the general selection value for eachselected search result; and ranking the search results of the pluralityof search results according to the respective adjusted scores for theselected search results and the respective scores for search resultsthat were not selected.
 11. The method of claim 10, wherein analyzingthe recent search activity period of the user comprises determining anamount of time elapsed between selection of a first search result in therecent search activity period and selection of a second search result inthe recent search activity period.
 12. The method of claim 10, whereineach of one or more of the selections of the particular search result isweighted by a respective weight based on an amount of time that a userviewed a document referred to by the particular search result.
 13. Themethod of claim 10, wherein the category relevance for each selectedsearch result is statistically significant.
 14. The method of claim 10,wherein adjusting the respective score for a particular selected searchresult comprises combining the category relevance for the particularselected search result with an information retrieval score of theparticular selected search result.
 15. The method of claim 14, whereincombining the category relevance for the selected search result with theinformation retrieval score of the particular selected search resultcomprises: multiplying the category relevance for the particularselected search result by the information retrieval score of theparticular selected search result, or adding the category relevance theparticular selected search result by the information retrieval score ofthe particular selected search result.
 16. The method of claim 10,wherein determining the short-term category of interest comprisesdetermining the short-term category if interest based on, at least, userspecific information.
 17. The method of claim 16, wherein the userspecific information includes at least one of demographic information ofthe user and location information of the user.
 18. The method of claim10, wherein: the category selection value for the particular searchresult is a first click-through rate for the particular search resultwhen the particular search result is presented in response to the queryand to a first group of users having an interest that matches theshort-term category of interest; the general selection value for theparticular search result is a second click-through rare for theparticular search result when the particular search result is presentedin response to the query and to a second group of users that have anycategory of interest, the second group of users having at least one userthat is not included in the first group of users; and the categoryrelevance is based on a difference between the first click-through rateand the second click-through rate.
 19. A system comprising: dataprocessing apparatus programmed to perform operations comprising:analyzing a recent search activity period of a user to determine ashort-term category of interest of the user, wherein the analyzingcomprises comparing queries submitted by the user during the recentsearch activity period with categories of selected search results thatwere selected by the user during the recent search activity period;obtaining a plurality of search results responsive to a query, each ofthe plurality of search results having a respective score; for eachparticular search result of a first plurality of the search results:calculating a category selection value for the particular search result,wherein the category selection value for the particular search result isbased on a measure of a count of selections of the particular searchresult as a portion of combined selection counts for search resultsresponsive to the query for a plurality of users for the short-termcategory of interest; calculating a general selection value for theparticular search result, wherein the general selection value for theparticular search result is based on a measure of a count of selectionsof the particular search result as a portion of combined selectioncounts for search results responsive to the query for a plurality ofusers for any category of interest; calculating a category relevance forthe particular search result, wherein the respective category relevancefor the particular search result is based on a difference between therespective category selection value for the search result and therespective general selection value for the particular search result;selecting a one or more search results of the first plurality of searchresults, each selected search result being selected based on theselected search result having a category relevance that exceeds athreshold; adjusting the respective score for each of the selectedsearch results based on, at least, the category selection value for eachselected search result and the general selection value for each selectedsearch result; and ranking the search results of the plurality of searchresults according to the respective adjusted scores for the selectedsearch results and the respective scores for search results that werenot selected.
 20. The system of claim 19, wherein analyzing the recentsearch activity period of the user comprises determining an amount oftime elapsed between selection of a first search result in the recentsearch activity period and selection of a second search result in therecent search activity period.
 21. The system of claim 19, wherein eachof one or more of the selections of the particular search result isweighted by a respective weight based on an amount of time that a userviewed a document referred to by the particular search result.
 22. Thesystem of claim 19, wherein the category relevance for each selectedsearch result is statistically significant.
 23. The system of claim 19,wherein adjusting the respective score for a particular selected searchresult comprises combining the category relevance for the selectedsearch result with an information retrieval score of the particularselected search result.
 24. The system of claim 23, wherein combiningthe category relevance for the particular selected search result withthe information retrieval score of the particular selected search resultcomprises: multiplying the category relevance for the particularselected search result by the information retrieval score of theparticular selected first search result, or adding the categoryrelevance for the selected search result by the information retrievalscore of the particular selected search result.
 25. The system of claim19, wherein determining the short-term category of interest comprisesdetermining the short-term category of interest based on, at least, userspecific information.
 26. The system of claim 25, wherein the userspecific information includes at least one of demographic information ofthe user and location information of the user.
 27. The system of claim19, wherein: the category selection value for the particular searchresult is a first click-through rate for the particular search resultwhen the particular search result is presented in response to the queryand to a first group of users having an interest that matches theshort-term category of interest; the general selection value for theparticular search result is a second click-through rare for theparticular search result when the particular search result is presentedin response to the query and to a second group of users that have anycategory of interest, the second group of users having at least one userthat is not included in the first group of users; and the categoryrelevance is based on a difference between the first click-through rateand the second click-through rate.